research-article

Machine Learning for the Classification of Obesity Levels Based on Lifestyle Factors

Authors:

Balbir SinghAuthors Info & Claims

ICCBDC '23: Proceedings of the 2023 7th International Conference on Cloud and Big Data Computing

Pages 28 - 33

https://doi.org/10.1145/3616131.3616135

Published: 02 October 2023 Publication History

Abstract

In recent years, the prevalence of obesity and its related co-morbidities have been increasing significantly. Therefore, it is an important challenge to pursue an early prediction of obesity risk that could help in reducing the pace of obesity rise when appropriate interventions are placed, accordingly. The prediction and classification of obesity depend on different factors such as body mass index (BMI) and lifestyle aspects, including eating habits. By focusing on these lifestyles and eating habit factors, we can develop a more holistic approach to weight management and prevention of obesity. The aim of this paper is to propose a machine-learning model that can classify weight levels using lifestyle variables without relying on BMI which enables us to investigate how lifestyle factors affect different levels of weight categorization. Although BMI is the most widely used estimation of obesity, there are other factors that can contribute to gaining weight such as lifestyle factors. The accuracy of our lifestyle-based model reached 75% excluding weight, height, and family history. Our model could serve as a starting point for using an interpretable machine learning model to better understand the effect of lifestyle factors on obesity levels.

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Cited By

Tawfik HSingh BKhater T(2024)Interpretable RNN for Prediction & Understanding of Childhood Obesity: A Scenario from the UK Millennium Cohort StudyProceedings of the 2024 8th International Conference on Cloud and Big Data Computing10.1145/3694860.3694867(49-53)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.1145/3694860.3694867
Khater THussain AMahmoud SYasen S(2023)Explainable AI for Breast Cancer Detection: A LIME-Driven Approach2023 16th International Conference on Developments in eSystems Engineering (DeSE)10.1109/DeSE60595.2023.10469341(540-545)Online publication date: 18-Dec-2023
https://doi.org/10.1109/DeSE60595.2023.10469341

Index Terms

Machine Learning for the Classification of Obesity Levels Based on Lifestyle Factors
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

ICCBDC '23: Proceedings of the 2023 7th International Conference on Cloud and Big Data Computing

August 2023

101 pages

ISBN:9798400707339

DOI:10.1145/3616131

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 October 2023

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ICCBDC 2023

ICCBDC 2023: 2023 7th International Conference on Cloud and Big Data Computing

August 17 - 19, 2023

Manchester, United Kingdom

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Cited By

Tawfik HSingh BKhater T(2024)Interpretable RNN for Prediction & Understanding of Childhood Obesity: A Scenario from the UK Millennium Cohort StudyProceedings of the 2024 8th International Conference on Cloud and Big Data Computing10.1145/3694860.3694867(49-53)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.1145/3694860.3694867
Khater THussain AMahmoud SYasen S(2023)Explainable AI for Breast Cancer Detection: A LIME-Driven Approach2023 16th International Conference on Developments in eSystems Engineering (DeSE)10.1109/DeSE60595.2023.10469341(540-545)Online publication date: 18-Dec-2023
https://doi.org/10.1109/DeSE60595.2023.10469341

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